Then, a set of convolutional layers are applied and extract features \(\mathbf {y}^\text{(enc,$L$)}_1, \cdots , \mathbf {y}^\text{(enc,$L$)}_I\) . In particular, we adopt shortcut connections every other layer parallel to linear transformation before the activation function, as in ResNet [1]}. This helps the training of a deep neural network.
